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Business Analytics of Today

  • Amruta Bhaskar
  • Jun 15, 2021
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  • 799 Ansichten

Business analytics, a data management solution and business intelligence subset refers to the use of methodologies such as data mining, predictive analytics, and statistical analysis in order to analyze and transform data into useful information, identify and anticipate trends and outcomes, and ultimately make smarter, data-driven business decisions.

The main components of a typical business analytics dashboard include:

  • Data Aggregation: prior to analysis, data must first be gathered, organized, and filtered, either through volunteered data or transactional records
  • Data Mining: data mining for business analytics sorts through large datasets using databases, statistics, and machine learning to identify trends and establish relationships
  • Association and Sequence Identification: the identification of predictable actions that are performed in association with other actions or sequentially 
  • Text Mining: explores and organizes large, unstructured text datasets for the purpose of qualitative and quantitative analysis
  • Forecasting: analyzes historical data from a specific period in order to make informed estimates that are predictive in determining future events or behaviours
  • Predictive Analytics: predictive business analytics uses a variety of statistical techniques to create predictive models, which extract information from datasets, identify patterns, and provide a predictive score for an array of organizational outcomes
  • Optimization: once trends have been identified and predictions have been made, businesses can engage simulation techniques to test out best-case scenarios
  • Data Visualization: provides visual representations such as charts and graphs for easy and quick data analysis

The essentials of business analytics are typically categorized as either descriptive analytics, which analyzes historical data to determine how a unit may respond to a set of variables; predictive analytics, which looks at historical data to determine the likelihood of particular future outcomes; or prescriptive analytics, the combination of the descriptive analytics process, which provides insight on what happened, and predictive analytics process, which provides insight on what might happen, providing a process by which users can anticipate what will happen when it will happen, and why it will happen.

Some business analytics examples include the operation and management of clinical information systems in the healthcare industry, the tracking of player spending and development of retention efforts in casinos, and the streamlining of fast-food restaurants by monitoring peak customer hours and identifying when certain food items should be prepared based on assembly time.

Modern, high-quality business analytics software solutions and platforms are developed to ingest and process the enormous datasets that businesses encounter and can exploit for optimal business operations.

When you use these four types of analytics, your data can be cleaned, dissected, and absorbed in a way that makes it possible to create solutions for no matter what challenges your organization may face 

1.   Descriptive analytics: Interpretation of historical data and KPIs to identify trends and patterns. This allows for a big picture look of what happened in the past and what is happening currently using data aggregation and data mining techniques.

Many companies use descriptive analytics for a deeper look into the behaviour of customers and how they can target marketing strategies to those customers.

2.   Diagnostic analytics: Focuses on past performance to determine which elements influence specific trends.

This is done using drill-down, data discovering, data mining, and correlation to reveal the cause of specific events. Once an understanding is reached regarding the likelihood of the event, and why an event may occur, algorithms are used for classification and regression.

3.   Predictive analytics: Uses statistics to forecast and assess future outcomes using statistical models and machine learning techniques. This often takes the results of descriptive analytics to create models that determine the likelihood of specific outcomes.

This type is often used by sales and marketing teams to forecast opinions of specific customers based on social media data.

4.   Prescriptive analytics: Uses past performance data to recommend how to handle similar situations in the future. Not only does this type of business analytics determine outcomes, but it can also recommend the specific actions that need to occur to have the best possible result. This is often achieved using deep learning and complex neural networks.

This type of business analytics is often used to match various options to the real-time needs of a consumer.

There are a host of business analytics tools that can perform these advanced data analytics functions automatically, requiring few of the special analytical skills or deep knowledge of programming languages necessary in data science.

These tools help businesses organize and make use of the massive amount of data that modern enterprise cloud applications produce. These applications may include supply chain management (SCM), enterprise resource planning (ERP) and customer relationship management (CRM) tools.

Below are some popular business analytics tools:

  • Qlik, which has data visualization and automated data association features.
  • Splunk, which is especially popular for small and medium-sized businesses because of its intuitive user interface and data visualization features.
  • Sisense, which is known for its dynamic text analysis features and data warehousing
  • KNIME, which is known for its high-performance data pipelining and machine learning
  • Dundas BI, which is popular because of its automated trend forecasting and its user-friendly, drag-and-drop interface features.
  • TIBCO Spotfire, which is considered one of the more advanced BA tools and offers powerful automated statistical and unstructured text analysis.
  • Tableau Big Data Analytics, which is also highly regarded for its advanced unstructured text analysis and natural language processing (NLP) capabilities.

When choosing a business analytics tool, organizations should consider the sources they will be drawing data from, the nature of the data they will be analyzing, and usability. A good business analytics tool will be easy enough for the common business user, but also enables more advanced users to take advantage of its features.

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